Lya2pcf: An Efficient Pipeline to Estimate Two- and Three-Point Correlation Functions of the Lyman- forest

We present Lya2pcf, a pipeline designed to compute three-dimensional two-point and three-point correlation functions using Lyman- forest data. The code implements standard algorithms for computing the two-point correlation function with its distortion and covariance matrices; and it extends the two-point estimator to three-point correlations. Thanks to GPU optimization, Lya2pcf reduces computational time when compared to the widely used PICCA code. We apply Lya2pcf to data from SDSS DR16 and DESI Year-5 mocks, demonstrating overall performance gains. We show the first measurement of the anisotropic three-point correlation function on a large spectroscopic sample for all possible triangles with scales up to 80 Mpc/h. The fast computation and the resulting signal-to-noise ratio demonstrate the viability of incorporating three-point statistics into future cosmological analyses.